‍The ability to collect and process vast amounts of data using AI and ML is profoundly changing the investment banking landscape. Some of the benefits of AI include dynamic deal origination and buyers list building, market and data analysis, and responsive customer relationship management. As AI technology evolves, its applications in the financial sector are becoming more sophisticated, shifting from routine tasks to complex strategic roles. Learn more about the evolution of AI in finance, its current applications for investment banks and the future of AI development.

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Evolution of AI in Investment Banking: Historical context and technological advancements

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Over the past decade, AI and machine learning (ML) systems have made significant strides in their development. Today's AI systems excel at well-defined tasks that traditionally required human intelligence. Today's AI systems excel at well-defined or specialized tasks that traditionally required human intelligence.

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The concept of AI can be traced back to the 1940s and 1950s when pioneers like Alan Turing and John von Neumann laid the theoretical foundations for computation and the possibility of creating intelligent machines. The term "artificial intelligence" was coined at the Dartmouth Workshop in 1956. This event marked the birth of AI as a distinct field of research. In the 70s and 80s AI research shifted toward expert systems, which aimed to capture human expertise in specific domains using knowledge-based rules and reasoning. Later, the development of backpropagation and other learning algorithms for artificial neural networks led to the development of early neural network models and the birth of machine learning. AI research has made significant strides in the 21st century, thanks to advances in machine learning, deep learning, and the availability of massive datasets.

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These developments have been the building block for AI use in investment banking. Modern tools using “Large Language Models” are now enabling scanning markets faster than before. Natural language processing (NLP) enabled advancements in software tools make possible the extraction of insights from unstructured data, such as financial reports, news articles, and research papers. This results in significantly less work for investment banks in multiple common tasks such as  market trend prediction, market mapping, valuation, and the assessment of industry-specific risks.

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These capabilities of AI were not properly exploited until ChatGPT was launched and enabled a massive increase in the quality that an AI model can deliver. Now investment banks are increasingly integrating AI technologies into various aspects of their operations. The effective deployment of AI/ML solutions, such as asg, is becoming more widespread.

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Worldwide, the rate of adoption and deployment of AI has been on the rise in the last few years. 42 % of global enterprises had actively deployed AI into their business operations in 2023, and 40 % of them were actively exploring the opportunities to include AI into their operations. Singapore was the leading country with a total of 94 % of companies that were exploring or had deployed AI within their business. For China and India both the number was 86 %, while in the U.S. 71 % of companies had adopted AI or were planning to do so. European countries such as Germany (76 %) and France (71 %) are currently at around the same level as the U.S. (Statista, 2024.)

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Overview of AI's impact on investment banking

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AI and investment banking are currently developing in synergy, driving significant benefits for investment banks. Firstly, AI enables investment banks to accelerate the process of searching suitable investment targets. Secondly, AI streamlines investment banking by automating tasks like analytics, predictive modeling and reporting. Third, machine learning in investment banking is transforming banking professionals’ capability to optimize their investment decisions.

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Will investment banking be automated? Currently, AI is changing the data management processes with an increasing speed, with an ability to execute many time-consuming manual tasks faster. However, not all tasks can be automated, which applies especially to building and maintaining client relationships, complex strategic decisions as well as regulatory compliance. These areas have the need for human expertise and experience. Nevertheless, there are multiple areas of investment banking that are being positively impacted by the adoption of AI. Adopting these technologies allows analysts to concentrate on higher-value tasks with more comprehensive information.

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Here are some areas where AI has current implications:

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  • Automated Data Collection: One of the main benefits of AI is the efficiency increase in data collection. AI’s capabilities of handling data exceed human capabilities. This means higher productivity for deal origination and buyers list building, for example.
  • Analytics and Research: NLP algorithms can conduct research and analyze market trends by extracting insights from unstructured data sources, such as news articles and research reports. This helps investment banks to gain competitive advantage, so they can direct their focus into new prospective areas.
  • Predictive Analytics for Superior Decision-Making: AI is able to identify patterns and correlations in human behavior and market trends. AI can combine data points fast from social media, news articles, and other sources to build a picture of market activity changes. This helps simulate market scenarios, scan competitor behavior, and predict what sectors to invest in.
  • Streamlined Task Automation: AI can automate repetitive tasks and reduce analysts’ manual effort from cumbersome desktop search. Thus AI allows analysts to focus on higher-value tasks, including client communication and M&A process.
  • Tailored Client Engagement: AI is used for personalized messages. AI uses information from various sources to produce and deliver content relevant to the audience. Chatbots by AI can assist customers with basic transactions. AI can also produce personalized investment strategies and budget allocation.
  • Proactive Risk Management: Risk analysis is becoming more accurate with AI-powered algorithms, which have the capacity to predict economic, financial, and risk events, and detect fraudulent activities in real-time by monitoring transactions and identifying unusual patterns or behaviors. AI improves risk management and compliance, credit risk assessment and enables more accurate risk profiling.

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AI Applications in Investment Banking: Specific use cases and benefits

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AI’s ability to process vast amounts of data in real-time is unparalleled. Investment banks leverage AI to streamline data collection and analysis, significantly improving productivity in deal origination and buyer list building. Advanced AI tools can scan millions of data points quickly, identifying promising acquisition targets and investment opportunities with greater accuracy than traditional methods​.

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Next, we explore the following five use cases in more detail:

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  1. New customer acquisition
  2. Buy-side: Larger pool of acquisition targets 
  3. Sell-side: More prospective buyers
  4. Current customers: New types of projects
  5. Improving investment banking professionals’ work quality with AI

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1. New customer acquisition with AI 

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With AI’s advanced data processing capabilities, investment banks are able to find ideal customers better. AI tools are able to respond to search requests and filter the most promising prospects for analysts from a global pool of potential customers. For example, finding all of the worlds’ midsize office furniture manufacturers near retirement age, will happen within a couple of seconds. Therefore, finding companies with owners ready to sell is more convenient with AI.

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AI reduces the need for industry codes and gives access to company data directly from niche sectors with increased accuracy. In addition, AI software can leverage previous investment bank deal data to identify similar cases and customers from specific sectors. By analyzing the information from past deals, AI can provide a comprehensive list of companies with similarities with the customers involved in those deals. This allows investment banks to identify potential opportunities in a more data-driven manner.

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2. Buy-side: Larger pool of acquisition targets

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‍AI based tools make building buy-side acquisition candidate lists more efficient. AI enables sourcing acquisition targets from much larger amounts of data than before. Millions of data points and fast data analysis means that investment banks have more candidates to identify the best ones and to proceed to the next stage. 

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NLP algorithms also improve deal origination by finding missed companies based on all the data on their website and other sources. AI can scan thousands of firms in minutes while for the analyst or intern it would take weeks. To add to that, AI can help investors find companies based on their previous ideal deals, like Inven’s platform does.

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AI is also transforming client experiences. It enables both chatbots and sending customized messages in bulk to prospective customers. Bulk customized messages result in a higher response rate. AI can ease writing messages to support management of key customer relationships and increase customer satisfaction.  

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3. Sell-side: More prospective buyers

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AI can significantly improve existing customer workflows. For sell-side deals, AI algorithms can build a list of prospective buyers and assess the investor profile and characteristics more efficiently. AI makes building lists of buyers faster, with access to far more prospective buyers in the global market. 

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Finding buyers globally is possible for the first time, with all the world's investor data available in a manageable format from AI platforms. Investors do not need to go through multiple websites, news channels, M&A transaction databases (such as SeekingAlpha) and industry databases from multiple countries. They can simply utilize easy-to-use streamlined platform to find everything they need, including contact data, in one place. 

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4. Current customers: New types of projects

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AI-driven analytics frees M&A professionals from the burdens of routine tasks and allows them to allocate more time to serving their customers. This will enhance their customer experience. 

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AI is able to screen and find companies ten times faster, with an ability to identify their main operation locations. This makes it possible to build market maps of entire industries in hours instead of days (with tools like Inven). This enables bringing full market maps already to potential client pitches as well as doing market mapping projects at a significantly more cost-efficient way. 

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Buy-side projects become more profitable when investment banks are able to save valuable time during acquisition candidate list building. AI streamlines the process of identifying potential acquisition targets, allowing investment banks to allocate more resources to due diligence and deal analysis. This improved efficiency enables investment banks to capitalize on attractive investment opportunities quicker and with greater precision, ultimately enhancing their success rate in buy-side projects.

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5. Improving professionals' work quality with AI

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AI has many applications that improve the current work quality of investment banking professionals. One of the most significant advantages is its capability to augment acquisition target searches. 

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By leveraging AI's data processing capabilities, investment banks can identify and evaluate potential acquisition targets more comprehensively and efficiently. This enables them to discover hidden opportunities and make more informed decisions regarding potential investments.

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AI can help do things that were previously very difficult and time consuming, such as swiftly identifying companies from around the world. With AI's language processing abilities and depth in data analysis, M&A professionals significantly save valuable time and effort with scanning more markets. AI's automated data extraction and analysis will expand the consideration of potential deals. 

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AI will increase analysts’ productivity and reduce the amount of repetitive tasks, such as data entry and report generation. AI reduces expensive human labor from everyday rule-based tasks and moves more M&A professionals’ time into financial analysis and due diligence.

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The future: How AI is changing investment banking

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The use of AI is shifting at major asset management firms from routine tasks such as compliance and marketing to more strategic functions and decision-making. Asset managers are increasingly utilizing AI to inform investment choices, monitor portfolio managers' habits, and identify profitable opportunities. Future AI applications in the financial sector span various functions, including asset management, marketing, due diligence and compliance.

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For example, JP Morgan Chase has used a chatbot to analyze legal documents. Currently JPMorgan Chase plans to expand its use of a generative AI tool, "Moneyball," which provides insights based on historical market behavior and helps managers refine their strategies. The tool aids in identifying and correcting biased decisions and alerts portfolio managers to potentially questionable decisions.

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In the near future, portfolio managers will be able to refer to virtual analysts to monitor investment risks and optimize decision-making. AI can elevate human capabilities by alerting potential errors in analysts' evaluations, as well as detecting more profitable investment targets.

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In addition, tools like Inven are widely used in investment banks to ease the manual searching of potential acquisition targets and buyers. These tools make it possible to gain superior industry understanding, build a buyers list and acquisition target list faster, and screen targets with increased accuracy.

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Additionally, the financial sector’s systems are likely to become more integrated with GPT4 powered analysis in the future. This enables automating tasks which require extensive language processing, such as writing one-pagers, regulatory and compliance related tasks, compliance checks and contract management.

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Furthermore, AI is likely to assist investment banks in automated data cleaning. Additional efficiencies could be reached in Due Diligence and parts of Financial Due Diligence (FDD). However, these still require custom software that are not yet widely available.

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Challenges and Considerations: Risks and ethical considerations

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AI provides more accurate data and data processing capabilities than the average person, which enriches professional decisions. Especially discussions around ethical considerations, bias, privacy and its impact have been raised by researchers and policymakers. In any case, it is good to note that while AI offers significant advantages, it brings forth new challenges and potential risks.

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Possible risks in M&A decision-making based on AI primarily stem from over-reliance on AI systems without complete understanding of its models. Understanding AI/ML-based financial decisions can be difficult as algorithms may reveal correlations without a clear picture of underlying causality. Sudden data fluctuations in disruptive events may break established correlations and affect these models, potentially leading to inaccurate decisions. 

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AI models are built on data sets which essentially means that the developers of these models are always making choices about the meaning of certain events, what information to include, and what to leave out. The structure of the models defines that their reliability depends on the human decisions about the data they are “fed”. It is possible that the information is flawed in some relevant areas, or that some missing data points go unnoticed, which could make decisions made by the models skewed.

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Therefore, all subsequent analysis and final decision-making should include human oversight. We want to make sure we understand the models that we base major financial decisions on. In addition, proper governance, cybersecurity, continuous monitoring, and a comprehensive understanding of AI limitations are crucial to mitigating these risks. However, the benefits of AI and machine learning for investment banking are yet to be fully realized.

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Conclusion

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Overall, AI continues to evolve and provide a competitive edge in the financial industry. AI makes it possible for investment banks to use data far more efficiently and improve their strategic decisions. The use of AI requires that the models are being monitored with consideration, and decisions are being evaluated also independently of the models.

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AI can save analysts’ time from manual tasks. Furthermore, access to better data and data analytics ultimately leads to improved financial outcomes. Investment bankers gain more time to foster client relationships, explore new opportunities, and provide personalized services to achieve higher customer satisfaction.

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